Data from Validating Wave 1 (2014) Urinary Cotinine and TNE-2 Cut-points for Differentiating Wave 4 (2017) Cigarette Use from Non-use in the United States Using Data from the PATH Study
Year: 2023
Type: other
Abstract: Background: Sex and racial/ethnic identity-specific cut-points for validating tobacco use using Wave 1 (W1) of the Population Assessment of Tobacco and Health (PATH) Study were published in 2020. The current study establishes predictive validity of the W1 (2014) urinary cotinine and total nicotine equivalents-2 (TNE-2) cut-points on estimating Wave 4 (W4; 2017) tobacco use. Methods: For exclusive and polytobacco cigarette use, weighted prevalence estimates based on W4 self-report alone and with exceeding the W1 cut-point were calculated to identify the percentage missed without biochemical verification. Sensitivity and specificity of W1 cut-points on W4 self-reported tobacco use status were examined. ROC curves were used to determine the optimal W4 cut-points to distinguish past 30-day users from non-users, and evaluate whether the cut-points significantly differed from W1. Results: Agreement between W4 self-reported use and exceeding the W1 cut-points was high overall and when stratified by demographic subgroups (0.7%–4.4% of use was missed if relying on self-report alone). The predictive validity of using the W1 cut-points to classify exclusive cigarette and polytobacco cigarette use at W4 was high (>90% sensitivity and specificity, except among polytobacco Hispanic smokers). Cut-points derived using W4 data did not significantly differ from the W1-derived cut-points [e.g., W1 exclusive = 40.5 ng/mL cotinine (95% confidence interval, CI: 26.1–62.8), W4 exclusive = 29.9 ng/mL cotinine (95% CI: 13.5–66.4)], among most demographic subgroups. Conclusions: The W1 cut-points remain valid for biochemical verification of self-reported tobacco use in W4. Impact: Findings from can be used in clinical and epidemiologic studies to reduce misclassification of cigarette smoking status.
Authors: Kathryn C. Edwards, Asia Khan, Eva Sharma, Lanqing Wang, June Feng, Benjamin C. Blount, Connie S. Sosnoff, Danielle M. Smith, Maciej L. Goniewicz, Jennifer Pearson, Andrea C. Villanti, Cristine D. Delnevo, Michelle T. Bover-Manderski, Dorothy K. Hatsukami, Raymond Niaura, Colm Everard, Heather L. Kimmel, Kara Duffy, Brian L. Rostron, Arseima Y. Del Valle-Pinero, Dana M. van Bemmel, Cassandra A. Stanton, Andrew Hyland
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4384828463